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pacman::p_load(maptools, sf, raster, spatstat, tmap, tidyverse, arrow, lubridate, spNetwork, classInt)Take Home Exercise 01
Project Overview
In this take-home exercise, I will be finding the geographical and spatio-temporal distribution of Grab hailing service locations in Singapore.
The distribution will be analysed using the following, which will be displayed on the openstreetmap of Singapore:
Traditional Kernel Density Estimation (KDE) layers
Network Kernel Density Estimation (NKDE) or Temporal Network Kernel Density Estimation (TNKDE)
Loading the packages
In this section, I will be loading the following packages:
maptools
sf
raster
spatstat
tmap
tidyverse
arrow
lubridate
spNetwork
classInt
viridis
Traditional Kernel Density Estimation
Extraction of the Coastal Outline of Mainland Singapore
Now, we can use the data for Singapore’s outline. The data can be found on data.gov.sg and is the Master Plan Subzone Boundary 2014.
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mpsz_sf <- st_read(dsn = "../../data/data",
layer = "MP14_SUBZONE_WEB_PL")Reading layer `MP14_SUBZONE_WEB_PL' from data source
`D:\KrisLBT\IS415-GAA\data\data' using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21We have to check the CRS to ensure that it is in the correct coordinates system.
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st_crs(mpsz_sf)Since it is in the wrong CRS, we have to correct it to SVY21.
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mpsz_sf <- st_transform(mpsz_sf, 3414)Now, we can plot the island.
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plot(mpsz_sf)We notice that the planning subzone includes areas where Grab does not serve due to lack of bridges. Hence, any roads in unavailable areas would either not be useful or actually distort the network analysis.
Most of the areas unserved by Grab are the outer islands. For this, we can identify which of the outer islands to exclude from our analysis.
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outer_islands<- filter(mpsz_sf, grepl('ISLAND', PLN_AREA_N))
plot(outer_islands["SUBZONE_N"])However, we realise that not all the islands are unavailable to Grab drivers. Grab drivers are able to pick up and drop off at Sentosa.
Since 2019, Grab drivers have been allowed entry to Jurong Island, provided they have a security pass. However, given that very few drivers are allowed entry and the fact that this region is generally inaccessible to the public, we will be excluding them.
Hence, the islands we need to exclude are:
- Semakau
- Southern Group
- Sudong
- North-eastern Islands
- Jurong Island and Bukom
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serviced_area <- mpsz_sf %>%
filter(!grepl("SEMAKAU|SOUTHERN GROUP|SUDONG|NORTH-EASTERN ISLANDS|JURONG ISLAND AND BUKOM", SUBZONE_N))
plot(serviced_area)Now, we can blend away the boundaries.
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sg_sf <- serviced_area %>%
st_union()Plot the sg_sf and we now see we have the CoastalOutline of Singapore.
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plot(sg_sf)We can save it as the CoastalOutline as a shapefile and read it again
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# writing the CoastalOutline
st_write(sg_sf, 'data/CoastalOutline.shp')Show the code
# reading the CoastalOutline
sg_sf <- st_read(dsn='data/',
layer = "CoastalOutline")Reading layer `CoastalOutline' from data source
`D:\KrisLBT\IS415-GAA\Take_Home_Exercises\Take_Home_Exercise_1\data'
using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 21494.3 xmax: 55941.94 ymax: 50256.33
Projected CRS: SVY21 / Singapore TMHandling of the Grab Posisi data
Reading the Grab Posisi Data
I will now be loading the Grab Posisi data
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df <- read_parquet(file="../../data/data/GrabPosisi/part-00000.snappy.parquet")
df1 <- read_parquet(file="../../data/data/GrabPosisi/part-00001.snappy.parquet")
df2 <- read_parquet(file="../../data/data/GrabPosisi/part-00002.snappy.parquet")
df3 <- read_parquet(file="../../data/data/GrabPosisi/part-00003.snappy.parquet")
df4 <- read_parquet(file="../../data/data/GrabPosisi/part-00004.snappy.parquet")
df5 <- read_parquet(file="../../data/data/GrabPosisi/part-00005.snappy.parquet")
df6 <- read_parquet(file="../../data/data/GrabPosisi/part-00006.snappy.parquet")
df7 <- read_parquet(file="../../data/data/GrabPosisi/part-00007.snappy.parquet")
df8 <- read_parquet(file="../../data/data/GrabPosisi/part-00008.snappy.parquet")
df9 <- read_parquet(file="../../data/data/GrabPosisi/part-00009.snappy.parquet")Handling the Grab Posisi data
Time stamp
There was supposed to be a datetime stamp but it wasn’t in that format. As such, the data type needs to be changed.
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df$pingtimestamp <- as_datetime(df$pingtimestamp)
df1$pingtimestamp <- as_datetime(df1$pingtimestamp)
df2$pingtimestamp <- as_datetime(df2$pingtimestamp)
df3$pingtimestamp <- as_datetime(df3$pingtimestamp)
df4$pingtimestamp <- as_datetime(df4$pingtimestamp)
df5$pingtimestamp <- as_datetime(df5$pingtimestamp)
df6$pingtimestamp <- as_datetime(df6$pingtimestamp)
df7$pingtimestamp <- as_datetime(df7$pingtimestamp)
df8$pingtimestamp <- as_datetime(df8$pingtimestamp)
df9$pingtimestamp <- as_datetime(df9$pingtimestamp)Merging them into 1 data frame
Merging the origin data frames into 1 origin data frame
For easier handling, we can merge the data frames together.
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origin_df_all <- df %>%
rbind(df1) %>%
rbind(df2) %>%
rbind(df3) %>%
rbind(df4) %>%
rbind(df5) %>%
rbind(df6) %>%
rbind(df7) %>%
rbind(df8) %>%
rbind(df9)Extraction of origin and destination
Extraction of origin
Now, I will be extracting the origin.
The way I did it below is by performing the following steps
- arrange it according to the trj_id (unique id determining where the person wants to at that point)
- arranging it according to the time stamp (beginning and end)
- getting the first row - mutating the data to weekday, start hour and the day
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origin_df <- origin_df_all %>%
group_by(trj_id) %>%
arrange(pingtimestamp) %>%
filter(row_number()==1) %>%
mutate(weekday = wday(pingtimestamp,
label=TRUE,
abbr=TRUE),
start_hr= factor(hour(pingtimestamp)),
day = factor(mday(pingtimestamp)))Writing of origin data to rds
Now, I will write the origin data to rds for easier future handling.
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write_rds(origin_df, "data/rds/origin_dfs.rds")Reading of RDS data
Now, we can clear the data in our environment and just read the rds data we saved.
Reading for origin data
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origin_df <- read_rds("data/rds/origin_dfs.rds")Analysis of Origins
Visualising the Frequency Distribution
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ggplot(data=origin_df,
aes(x=weekday)) +
geom_bar()
As can be seen, the number of trips daily seem to be fairly equally distributed throughout the week.
Visualising the Origins as a Point Map Symbol
We can visualise the geospatial distribution of the origin points.
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origins_sf <- st_as_sf(origin_df, coords = c("rawlng", "rawlat"),
crs=4326) %>%
st_transform(crs=3414)Now, we can visualise it using the code chunk below:
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tm_shape(mpsz_sf)+
tm_polygons()+
tm_shape(origins_sf) +
tm_dots()Convert sf data frames to sp’s Spatial class
We can convert these data to sp’s Spatial* class and then convert into a generic sp format.
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sg <- as_Spatial(sg_sf)
mpsz <- as_Spatial(mpsz_sf)
origins <- as_Spatial(origins_sf) And into SpatialPolygon format
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sg_sp <- as(sg, "SpatialPolygons")
origins_sp <- as(origins, "SpatialPoints")We then convert it to ppp format.
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origins_ppp <- as(origins_sp, "ppp")
origins_pppPlanar point pattern: 28000 points
window: rectangle = [3628.24, 49845.23] x [25198.14, 49689.64] unitsNow, I will plot origins_ppp.
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plot(origins_ppp)We must check for duplicates in the data using the code chunk below:
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any(duplicated(origins_ppp))[1] FALSEAs there are no duplicates within the data, we do not have to apply any more transformation to the data.
Create owin data
Now, we can convert the Coastal Outline to owin data and plot it out.
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sg_owin <- as(sg_sp, "owin")
plot(sg_owin)
Combining events
We will now extract Grab origins within the Singapore CoastalOutline
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origins_SG_ppp = origins_ppp[sg_owin]Show the code
plot(origins_SG_ppp)
Traditional Kernel Analysis
Now we can begin the traditional kernel analysis
Country level analysis
We can begin by trying to find all of the best kernel and bandwidths for the purposes of our analysis. To do this, we need to find the one with the tightest clusters.
In R, we have 4 possible kernel density bandwidths: diggle, ppl, scott and CvL. All of them will be test below:
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kde_origins_SG.bw <- density(origins_SG_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_origins_SG.ppl <- density(origins_SG_ppp,
sigma=bw.ppl,
edge=TRUE,
kernel="gaussian")
kde_origins_SG.CvL <- density(origins_SG_ppp,
sigma=bw.CvL,
edge=TRUE,
kernel="gaussian")
kde_origins_SG.scott <- density(origins_SG_ppp,
sigma=bw.scott,
edge=TRUE,
kernel="gaussian")We may also plot them out:
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par(mfrow=c(2,2))
plot(kde_origins_SG.bw)
plot(kde_origins_SG.ppl)
plot(kde_origins_SG.CvL)
plot(kde_origins_SG.scott)Now, we can check for the tightness of these clusters:
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# tightness of diggle
bw.diggle(origins_SG_ppp)
# tightness of ppl
bw.ppl(origins_SG_ppp)
# tightness of CvL
bw.CvL(origins_SG_ppp)
# tightness of scott
bw.scott(origins_SG_ppp)We can observe that the smallest sigma value is from diggle, suggesting that it has the tightest clusters of all the bandwidth. Hence, we will continue using diggle for this analysis.
We can also test for different kernels:
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par(mfrow=c(2,2))
plot(density(origins_SG_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian"),
main="Gaussian")
plot(density(origins_SG_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="epanechnikov"),
main="Epanechnikov")
plot(density(origins_SG_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="quartic"),
main="Quartic")
plot(density(origins_SG_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="disc"),
main="Disc")
As they are identical, there is no need to choose a specific one.
Regions of Interest
We can tighten our analysis to include only areas of interest. From the figures above, we observe higher densities in the planning areas Changi, Jurong East, Woodlands and Marine Parade, we can filter out mpsz to get them.
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# extraction of Changi Airport
CA = mpsz_sf[mpsz_sf$PLN_AREA_N=="CHANGI",]
# extraction of Jurong East
JE = mpsz_sf[mpsz_sf$PLN_AREA_N=="JURONG EAST",]
# extraction of Woodlands
WL = mpsz_sf[mpsz_sf$PLN_AREA_N=="WOODLANDS",]
# extraction of Marine Parade
MP = mpsz_sf[mpsz_sf$PLN_AREA_N=='MARINE PARADE',]With this, we now have to perform the same functions we did in Create Owin Data.
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# turn them into spatial
CA_spatial <- as_Spatial(CA)
JE_spatial <- as_Spatial(JE)
WL_spatial <- as_Spatial(WL)
MP_spatial <- as_Spatial(MP)
# turn them into SpatialPolygons
CA_sp <- as(CA_spatial, "SpatialPolygons")
JE_sp <- as(JE_spatial, "SpatialPolygons")
WL_sp <- as(WL_spatial, "SpatialPolygons")
MP_sp <- as(MP_spatial, "SpatialPolygons")
# convert to owin
CA_owin = as(CA_sp, "owin")
JE_owin = as(JE_sp, "owin")
WL_owin = as(WL_sp, "owin")
MP_owin = as(MP_sp, "owin")From here, we can extract the events that occurred in these planning areas.
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origins_CA_ppp = origins_SG_ppp[CA_owin]
origins_JE_ppp = origins_SG_ppp[JE_owin]
origins_WL_ppp = origins_SG_ppp[WL_owin]
origins_MP_ppp = origins_SG_ppp[MP_owin]We can now perform the same analysis that we did above:
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kde_origins_CA <- density(origins_CA_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_origins_JE <- density(origins_JE_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_origins_WL <- density(origins_WL_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_origins_MP <- density(origins_MP_ppp,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian") We can also plot them out:
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par(mfrow=c(2,2))
plot(kde_origins_CA,
main="Changi")
plot(kde_origins_WL,
main="Woodlands")
plot(kde_origins_JE,
main="Jurong East")
plot(kde_origins_MP,
main="Marine Parade")
Clark and Evans test
Entire Singapore
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clarkevans.test(origins_SG_ppp,
correction="none",
clipregion="sg_owin",
alternative=c("clustered"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origins_SG_ppp
R = 0.28039, p-value < 2.2e-16
alternative hypothesis: clustered (R < 1)Changi
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clarkevans.test(origins_CA_ppp,
correction="none",
clipregion=NULL,
alternative=c("two.sided"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origins_CA_ppp
R = 0.13547, p-value < 2.2e-16
alternative hypothesis: two-sidedWoodlands
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clarkevans.test(origins_WL_ppp,
correction="none",
clipregion=NULL,
alternative=c("two.sided"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origins_WL_ppp
R = 0.31778, p-value < 2.2e-16
alternative hypothesis: two-sidedJurong East
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clarkevans.test(origins_JE_ppp,
correction="none",
clipregion=NULL,
alternative=c("two.sided"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origins_JE_ppp
R = 0.25797, p-value < 2.2e-16
alternative hypothesis: two-sidedMarine Parade
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clarkevans.test(origins_MP_ppp,
correction="none",
clipregion=NULL,
alternative=c("two.sided"),
nsim=99)
Clark-Evans test
No edge correction
Z-test
data: origins_MP_ppp
R = 0.51201, p-value < 2.2e-16
alternative hypothesis: two-sidedNetwork Kernel Density Estimation
While the above does show the distribution of the points and which areas are more concentrated than others, they do not account for the network of the roads. In this section, we will be finding the networks (roads) that are more densely used as origins points in Changi Airport and Marina East.
Data Handling
Handling of Road Data
Now, we can read the road data as provided by openstreetmap. Note that it provides data for Malaysia, Singapore and Brunei all at once.
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all_roads <- st_read(dsn='../../data/data/data',
layer = 'gis_osm_roads_free_1')We realise that it is in WGS 84, not in SVY21 so we have to transform the data.
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all_roads <- st_transform(all_roads, 3414)We can check the CRS again for all_roads.
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st_crs(all_roads)Since we’re only interested in the roads in mainland Singapore, we have to filter the roads.
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SG_roads <- st_intersection(all_roads, sg_sf)Show the code
st_write(SG_roads, 'data/SG_roads.shp')We can now read it from here.
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SG_roads <- st_read(dsn='data', layer= 'SG_roads')Reading layer `SG_roads' from data source
`D:\KrisLBT\IS415-GAA\Take_Home_Exercises\Take_Home_Exercise_1\data'
using driver `ESRI Shapefile'
Simple feature collection with 227969 features and 10 fields
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 2679.373 ymin: 23099.51 xmax: 50957.8 ymax: 50220.06
Projected CRS: SVY21 / Singapore TMGetting the Roads within the Subzones
Now, we can find the streets that exist only inside the subzones.
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CA_roads <- st_intersection(SG_roads, CA)
WL_roads <- st_intersection(SG_roads, WL)
JE_roads <- st_intersection(SG_roads, JE)
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CA_roadsSimple feature collection with 4007 features and 25 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 42577.65 ymin: 32548.05 xmax: 50286.8 ymax: 41649.2
Projected CRS: SVY21 / Singapore TM
First 10 features:
osm_id code fclass name ref oneway maxspeed layer
356 22617051 5113 primary Loyang Avenue <NA> F 70 0
360 22617159 5113 primary Loyang Avenue <NA> F 70 0
1348 22981700 5113 primary Telok Paku Road <NA> F 50 0
3201 34403286 5114 secondary Loyang Way <NA> F 50 0
3207 34403387 5122 residential Changi North Street 1 <NA> B 0 0
3208 34403405 5122 residential Changi North Crescent <NA> B 50 0
3209 34403417 5122 residential Changi North Rise <NA> B 0 0
4001 42063713 5141 service <NA> <NA> F 0 0
4002 42063715 5141 service <NA> <NA> B 0 0
4003 42063716 5141 service <NA> <NA> B 0 0
bridge tunnel OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
356 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
360 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
1348 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
3201 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
3207 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
3208 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
3209 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
4001 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
4002 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
4003 F F 221 2 CHANGI WEST CHSZ02 N CHANGI
PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
356 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
360 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
1348 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
3201 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
3207 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
3208 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
3209 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
4001 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
4002 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
4003 CH EAST REGION ER 7460F2CFB1D7D36C 2014-12-05 44092.34
Y_ADDR SHAPE_Leng SHAPE_Area geometry
356 38928.66 14918.11 4848517 LINESTRING (44617.88 41088....
360 38928.66 14918.11 4848517 LINESTRING (43851.2 39632.9...
1348 38928.66 14918.11 4848517 LINESTRING (45352.67 41113....
3201 38928.66 14918.11 4848517 LINESTRING (43761.28 39560....
3207 38928.66 14918.11 4848517 LINESTRING (43152.23 36906....
3208 38928.66 14918.11 4848517 LINESTRING (42843.89 36842....
3209 38928.66 14918.11 4848517 LINESTRING (43130.89 36812....
4001 38928.66 14918.11 4848517 LINESTRING (43433.23 37663....
4002 38928.66 14918.11 4848517 LINESTRING (43436.11 37657....
4003 38928.66 14918.11 4848517 LINESTRING (43511.5 37906.7...Show the code
WL_roadsSimple feature collection with 7165 features and 25 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 20613.4 ymin: 44814.83 xmax: 25593.19 ymax: 49200.77
Projected CRS: SVY21 / Singapore TM
First 10 features:
osm_id code fclass name ref oneway maxspeed layer
796 22773625 5115 tertiary Woodlands Avenue 6 <NA> F 40 0
819 22774351 5131 motorway_link <NA> <NA> F 50 0
825 22774532 5122 residential Woodlands Drive 14 <NA> F 40 0
826 22774537 5122 residential Woodlands Drive 53 <NA> B 50 0
827 22774541 5122 residential Woodlands Drive 43 <NA> F 40 0
832 22775173 5115 tertiary Woodlands Avenue 5 <NA> F 60 0
834 22775385 5113 primary Woodlands Avenue 12 <NA> F 70 0
835 22775386 5114 secondary Woodlands Avenue 1 <NA> F 50 0
836 22775389 5114 secondary Woodlands Avenue 1 <NA> F 50 0
3703 37584630 5111 motorway Seletar Expressway SLE F 90 1
bridge tunnel OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND
796 F F 282 4 WOODLANDS SOUTH WDSZ04 N
819 F F 282 4 WOODLANDS SOUTH WDSZ04 N
825 F F 282 4 WOODLANDS SOUTH WDSZ04 N
826 F F 282 4 WOODLANDS SOUTH WDSZ04 N
827 F F 282 4 WOODLANDS SOUTH WDSZ04 N
832 F F 282 4 WOODLANDS SOUTH WDSZ04 N
834 F F 282 4 WOODLANDS SOUTH WDSZ04 N
835 F F 282 4 WOODLANDS SOUTH WDSZ04 N
836 F F 282 4 WOODLANDS SOUTH WDSZ04 N
3703 T F 282 4 WOODLANDS SOUTH WDSZ04 N
PLN_AREA_N PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D
796 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
819 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
825 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
826 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
827 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
832 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
834 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
835 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
836 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
3703 WOODLANDS WD NORTH REGION NR 8A4E14DAC4ACE11C 2014-12-05
X_ADDR Y_ADDR SHAPE_Leng SHAPE_Area geometry
796 23609.57 45692.88 5211.384 1576001 LINESTRING (24007.3 45538.1...
819 23609.57 45692.88 5211.384 1576001 LINESTRING (22821.47 45515....
825 23609.57 45692.88 5211.384 1576001 LINESTRING (23444.28 46022....
826 23609.57 45692.88 5211.384 1576001 LINESTRING (23990.45 46088....
827 23609.57 45692.88 5211.384 1576001 LINESTRING (23447.47 46014....
832 23609.57 45692.88 5211.384 1576001 LINESTRING (24645.58 46021....
834 23609.57 45692.88 5211.384 1576001 LINESTRING (23836.85 45038....
835 23609.57 45692.88 5211.384 1576001 LINESTRING (23124.88 45989....
836 23609.57 45692.88 5211.384 1576001 LINESTRING (24156.4 45407.0...
3703 23609.57 45692.88 5211.384 1576001 LINESTRING (23684.88 44959....Show the code
JE_roadsSimple feature collection with 7131 features and 25 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 14254.68 ymin: 30994.22 xmax: 19398.25 ymax: 37289.81
Projected CRS: SVY21 / Singapore TM
First 10 features:
osm_id code fclass name ref oneway maxspeed
1084 22903885 5114 secondary Penjuru Road <NA> F 60
1085 22903886 5121 unclassified Penjuru Road <NA> F 60
1443 23101520 5112 trunk Jalan Buroh <NA> F 70
3791 39959160 5112 trunk Jalan Buroh <NA> F 70
3792 39959161 5112 trunk Jalan Buroh <NA> F 70
5003 70583774 5115 tertiary Pandan Road <NA> B 50
5004 70583780 5121 unclassified Penjuru Lane <NA> B 50
5005 70583783 5114 secondary Tanjong Penjuru <NA> B 50
5006 70583786 5114 secondary Tanjong Penjuru <NA> F 50
5007 70583790 5121 unclassified Tanjong Penjuru Crescent <NA> B 50
layer bridge tunnel OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND
1084 0 F F 80 10 PENJURU CRESCENT JESZ10 N
1085 0 F F 80 10 PENJURU CRESCENT JESZ10 N
1443 1 T F 80 10 PENJURU CRESCENT JESZ10 N
3791 1 T F 80 10 PENJURU CRESCENT JESZ10 N
3792 0 F F 80 10 PENJURU CRESCENT JESZ10 N
5003 0 F F 80 10 PENJURU CRESCENT JESZ10 N
5004 0 F F 80 10 PENJURU CRESCENT JESZ10 N
5005 0 F F 80 10 PENJURU CRESCENT JESZ10 N
5006 0 F F 80 10 PENJURU CRESCENT JESZ10 N
5007 0 F F 80 10 PENJURU CRESCENT JESZ10 N
PLN_AREA_N PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D
1084 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
1085 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
1443 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
3791 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
3792 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
5003 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
5004 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
5005 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
5006 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
5007 JURONG EAST JE WEST REGION WR BC263278706376DE 2014-12-05
X_ADDR Y_ADDR SHAPE_Leng SHAPE_Area geometry
1084 17651.31 31799.61 9876.049 3049720 LINESTRING (16991.44 32621....
1085 17651.31 31799.61 9876.049 3049720 LINESTRING (16738.48 31589....
1443 17651.31 31799.61 9876.049 3049720 LINESTRING (16290.72 32753....
3791 17651.31 31799.61 9876.049 3049720 LINESTRING (19056.98 32186....
3792 17651.31 31799.61 9876.049 3049720 LINESTRING (18978.73 32113....
5003 17651.31 31799.61 9876.049 3049720 LINESTRING (18411.82 31559....
5004 17651.31 31799.61 9876.049 3049720 LINESTRING (17208.01 32320....
5005 17651.31 31799.61 9876.049 3049720 LINESTRING (16869.64 32131....
5006 17651.31 31799.61 9876.049 3049720 LINESTRING (17506.95 32049....
5007 17651.31 31799.61 9876.049 3049720 LINESTRING (17697.91 31764....Show the code
MP_roadsSimple feature collection with 2864 features and 25 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 32697.12 ymin: 29763 xmax: 37589.95 ymax: 32843.37
Projected CRS: SVY21 / Singapore TM
First 10 features:
osm_id code fclass name ref oneway
922 22810641 5114 secondary Tanjong Katong Road South <NA> F
1202 22930502 5113 primary Marina East Drive <NA> F
3760 39477475 5111 motorway East Coast Parkway ECP F
3761 39477476 5111 motorway East Coast Parkway ECP F
4078 44122367 5152 cycleway Park Connector <NA> B
4097 44133236 5153 footway Underpass to Meyer Road <NA> B
4140 44488255 5153 footway Katong Park <NA> B
4141 44488257 5121 unclassified East Coast Park Service Road <NA> B
4142 44488265 5114 secondary Tanjong Katong Road South <NA> F
5509 74729034 5111 motorway East Coast Parkway ECP F
maxspeed layer bridge tunnel OBJECTID SUBZONE_NO SUBZONE_N
922 50 0 F F 44 5 MARINA EAST (MP)
1202 60 0 F F 44 5 MARINA EAST (MP)
3760 90 1 T F 44 5 MARINA EAST (MP)
3761 80 0 F F 44 5 MARINA EAST (MP)
4078 0 0 F F 44 5 MARINA EAST (MP)
4097 0 -1 F T 44 5 MARINA EAST (MP)
4140 0 -1 F T 44 5 MARINA EAST (MP)
4141 50 0 F F 44 5 MARINA EAST (MP)
4142 50 1 T F 44 5 MARINA EAST (MP)
5509 90 0 F F 44 5 MARINA EAST (MP)
SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C REGION_N REGION_C
922 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
1202 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
3760 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
3761 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
4078 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
4097 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
4140 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
4141 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
4142 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
5509 MPSZ05 N MARINE PARADE MP CENTRAL REGION CR
INC_CRC FMEL_UPD_D X_ADDR Y_ADDR SHAPE_Leng SHAPE_Area
922 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
1202 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
3760 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
3761 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
4078 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
4097 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
4140 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
4141 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
4142 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
5509 1575868DF0D30E32 2014-12-05 33715.7 30512.25 6657.151 1590340
geometry
922 LINESTRING (35230.6 31057.3...
1202 LINESTRING (33760.43 30638....
3760 LINESTRING (33860.36 30899....
3761 LINESTRING (33706.71 30894....
4078 LINESTRING (34789.2 30864.6...
4097 LINESTRING (34772.07 30914....
4140 LINESTRING (34005.49 30906....
4141 LINESTRING (35255.73 30985,...
4142 LINESTRING (35227.24 31091....
5509 LINESTRING (34854.12 30932....We notice that all of the above are geometry type geometry and not a linestring. We can convert them with the following code chunk:
Show the code
CA_roads <- CA_roads %>%
st_cast("LINESTRING")
WL_roads <- WL_roads %>%
st_cast("LINESTRING")
JE_roads <- JE_roads %>%
st_cast("LINESTRING")
MP_roads <- MP_roads %>%
st_cast("LINESTRING")Extraction of the Events Within the Subzones
Before we can conduct analysis on the network, we will also need to constrict the events to exclusively the ones that occurred within these two subzones.
Show the code
#extraction of origin events that occurred within Changi and Marine Parade
CA_origins <- st_intersection(origins_sf, CA)
WL_origins <- st_intersection(origins_sf, WL)
JE_origins <- st_intersection(origins_sf, JE)
MP_origins <- st_intersection(origins_sf, MP)We can now plot this data. In this instance, we can use Changi as an example:
Show the code
tmap_mode('view')
tm_shape(CA_origins)+
tm_dots() +
tm_shape(CA_roads) +
tm_lines()